Cooperative Perception With Localization Uncertainty: A Cubature Split Covariance Intersection Framework

Cooperative perception techniques empower connected and automated vehicles (CAVs) perception capabilities through Vehicle-to-Everything (V2X) communication. However, this advancement introduces a significant influx of information within CAVs, posing a new challenge in managing potentially intricate aspects of asynchrony and correlation in this information landscape. In this context, this paper proposes a cooperative perception algorithm that considers localization uncertainty and information correlation and asynchrony. Specifically, a hierarchical split covariance intersection (SCI) approach is proposed to efficiently fuse the information from local sensors and connected devices. To bridge the reference disparities of information among CAVs, we incorporate localization uncertainty into the connected information fusion through a coordinate transformation approach based on the cubature rule, which unifies the references. Then, the covariance boundedness of the whole proposed algorithm is theoretically analyzed, demonstrating to some extent the safety guaranteed by our algorithm in practical applications. Finally, we build a high-fidelity driving simulator and collected real trajectory data from 80 drivers. The simulation and driver data testing results show the effectiveness and superiority of the proposed algorithm.